BIRN - Biomedical Informatics Research Network » Brain Morphometry BIRN

Morphometry BIRN Multi-site Multi-session Structural MRI Data

helmer — Thu, 20 Aug 2009 00:40:41 +0000

This data set consists of spoiled gradient-recalled echo magnetic resonance imaging data from five healthy volunteers (four males and one female) scanned twice at four sites having 1.5T systems from different vendors (Siemens, GE, Marconi Medical Systems). Some subjects were also scanned a single time at another site. One subject was only scanned twice at three sites (subject 73213384) and once at another site. For each subject, four Fast Low-Angle Shot (FLASH) scans with flip angles of 3, 5, 20, and 30 degrees were obtained in a single scan session, from which tissue proton density and T1 maps can be derived. These data were acquired to investigate various metrics of within-site and across-site reproducibility. This study was sponsored by the Brain Morphometry testbed (MBIRN) of the Biomedical Informatics Research Network (BIRN). The images have been defaced so that no facial features can be reconstructed from these data.

To access this dataset: mBIRN Calib page at XNAT

MRI Studio

helmer — Tue, 11 Aug 2009 22:36:14 +0000

Status: In production

MRI Studio (https://www.mristudio.org/ ) is a Windows-based software package, developed at Johns Hopkins University that includes support for 3D visualization, the processing of diffusion tensor data, and image registration. The package supports a number of different image data types from Philips PAR and REC files to DICOM. MRI Studio can be obtained from the S. Mori group website here. The package is supported through several mailing lists (https://lists.mristudio.org/mailman/listinfo).

To access this tool: https://www.mristudio.org/

Large Deformation Diffeomorphic Metric Mapping (LDDMM)

helmer — Tue, 11 Aug 2009 16:35:09 +0000

Status: In production

The Center for Imaging Science (CIS) at Johns Hopkins University (JHU) has been successful in providing Shape Analysis tools to the BIRN community. These tools are able to compare complex-shaped brain structures and detect differences over the entire surface of the structure. Large-scale shape-analysis processing has been established through the use of Large Deformation Diffeomorphic Metric Mapping (LDDMM, http://cis.jhu.edu/software/lddmm-volume/index.html) and the use of the TeraGrid.

The Large Deformation Diffeomorphic Metric Mapping (LDDMM) tool aims to quantify metric distances on anatomical structures in medical images. This allows for the direct comparison and quantization of morphometric changes due to, for example, disease or aging. As part of these efforts, the Center for Imaging Science at Johns Hopkins University developed techniques to not only compare images, but also to visualize the changes and differences.

Aspects of this work have been published:

Faisal Beg, Michael Miller, Alain Trouve, and Laurent Younes. Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision, Volume 61, Issue 2; February 2005.

M.I. Miller and A. Trouve and L. Younes, On the Metrics and Euler-Lagrange Equations of Computational Anatomy, Annual Review of Biomedical Engineering, 4:375-405, 2002.)

A significant source of error in brain image data is spatial distortions due to inhomogeneities in the main magnetic field (B0), which can arise due to imperfect gradient non-linearity, shimming, magnetic susceptibility effects and chemical shift. These effects can become particularly important for longitudinal studies in which different shim settings can result in substantially different distortions between scan sessions. Although these distortions are most pronounced in functional or diffusion-weighted imaging using echo planar imaging (EPI) sequences, the effect can be significant (on the order of several millimeters) even in conventional structural images.

Work by Susumu Mori’s group at the JHU showed that landmark-based distortion correction using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) algorithm can be a viable method to minimize B0 distortion. They have developed software to perform these operations. This software is currently in the testing phase.

To use this tool: http://www.nitrc.org/projects/lddmm-volume/

Efficient Longitudinal Upload of Depression in the Elderly (ELUDE)

helmer — Fri, 07 Aug 2009 22:43:31 +0000

The ELUDE dataset is a longitudinal study of late-life depression at Duke University. There are 281 depressed subjects and 154 controls included. An MR scan of each subject was obtained every 2 years for up to 8 years (total of 1093 scans). Clinical assessments occurred more frequently and consists of a battery of psychiatric tests including several depression-specific tests.

To access this data: http://nirlarc.duhs.duke.edu/nirle/

Multisite Imaging Research In the Analysis of Depression (MIRIAD)

helmer — Fri, 07 Aug 2009 22:29:10 +0000

A multiple institution study of structural MRI, including raw PD and T2 MRIs and derived measures of white matter changes, basal ganglia and other regions. Demographic and extensive clinical assessment data is available for each case.

To access this data: http://nirlarc.duhs.duke.edu/nirle/

Open Access Structural Imaging Series (OASIS)

helmer — Fri, 07 Aug 2009 22:27:49 +0000

This set consists of a cross-sectional collection of 416 human subjects aged 18 to 96, including individuals with Alzheimer’s disease; T1-weighted MRI scans obtained in single scan sessions are included. Additionally, a reliability data set is included containing 20 nondemented subjects imaged on a subsequent visit within 90 days of their initial session.

To access this data: http://central.xnat.org/app/template/XDATScreen_report_xnat_projectData.vm/search_element/xnat:projectData/search_field/xnat:projectData.ID/search_value/CENTRAL_OASIS_CS

XNAT

helmer — Fri, 07 Aug 2009 20:11:15 +0000

Status: In production

The Extensible Neuroimaging Archive Toolkit (XNAT) is an open source software platform designed to facilitate management and exploration of neuroimaging and related data. XNAT provides a web-based data management for diverse data sets. XML is used within XNAT both to define data types and to automatically generate the data store. Users of XNAT can employ pre-defined standard schemas or extend the schemas for custom deployment. The XML schema document defines relationships between different data types, thus enabling complex queries. XNAT provides the user with visualization tools, methods for data validation and integrity-checking, the ability to export data into a number of convenient formats and security features.

To access this tool: http://www.xnat.org/

XCEDE SPM Toolbox

helmer — Fri, 07 Aug 2009 19:59:58 +0000

Status: In production

Was created to capture the results from activation maps using the XCEDE schema. The toolbox supports SPM99 and SPM2 statistical structures and has been tested on SUN, LINUX, and Microsoft Windows operating systems to capture PET and fMRI analysis results and the associated analysis model specifications.

To access this tool: http://www.nitrc.org/frs/shownotes.php?release_id=551

LONI Pipeline

sruffins — Fri, 07 Aug 2009 19:50:31 +0000

Status: In production

Provides a visual programming interface that allows researchers to link many independently developed analysis programs into a processing “pipeline.”

The LONI Pipeline uses a simple, graphical environment to allow the user to easily buildup a complex data processing stream. The interface is intuitive and allows the user to build up their processing stream from a diverse set of programs. In addition, the Pipeline will automatically parallelize programs whenever possible to maximize efficiency when supercomputing resources are available. The LONI Pipeline has library definitions available for: AIR, MNI, BrainSuite, FSL, FreeSurfer, ITK, VTK, 3D Slicer.

To access this tool: http://www.loni.ucla.edu/Software/Pipeline

Gradient non-linearity distortion correction

helmer — Fri, 07 Aug 2009 18:39:36 +0000

Status: In production

The goal of this software is to provide a 3D correction of image distortions in MRI data due to non-linearity of the magnetic fields from the imaging gradient coils.

One of the challenges for a multi-site or longitudinal trial is to minimize image variations due to imaging gradient hardware. Imperfections in magnetic field gradient pulses can result in image warping and may limit the detection power of a study attempting to detect structural variations due to pathology. It has been shown that image distortions can significantly affect the quantitative measure of volume, shape, and boundary. The most common source of image distortion is imaging gradient non-linearity, which can distort the images not only in the in-plane directions, but also out of plane.

The Morphometry BIRN is making available a gradient non-linearity distortion correction algorithm, developed at the MGH and UCSD sites, that reduces image distortions and improves test-retest reproducibility of image intensity in multi-site structural MR data studies.

The manuscript from this work has been published:

Jovicich, J, Czanner, S, Greve, D, Haley, E, van der Kouwe, A, Gollub, R, Kennedy, D, Schmitt, F, Brown, G, Macfall, J, Fischl, B, Dale, A. Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data. NeuroImage 30:436-443, 2005.

The gradient unwarping software package is available here.

The scripts in this package use spherical harmonics information specific for each scanner gradient set. The tables of spherical harmonics must be obtained by each user from their scanner manufacturer. The scripts do not provide spherical harmonic information in any form (neither the spherical harmonics themselves nor the calculated displacement file) because this is proprietary information. Currently, the user must contact the vendor to get the gradient spherical harmonics information and then use BIRN scripts to create a displacement table (which is used by the image correction software).

The mBIRN makes available to the public a phantom specifically designed to measure the image distortions due to gradient non-linearities. Click here to get information about this phantom.

The gradients supported by this software include:

Cardiac Resonator Module (CRM) gradient coils from GE Medical Systems (maximum strength = 40 mT/m, slew rate = 150 T/m/s);
Brain Resonator Module (BRM) gradient coils from GE Medical Systems (22 mT/m, 120 mT/m/ms);
Sonata gradient coils from Siemens Medical Systems (40 mT/m, 200 T/m/s);
Avanto gradient coils from Siemens Medical Systems

To use this tool: http://www.nitrc.org/projects/grad_unwarp